Formation of electroless barrier and seed layers in a high aspect ratio through-Si vias using Au nanoparticle catalyst for all-wet Cu filling technology

An all-wet process was achieved using electroless deposition of barrier and Cu seed layers for fabrication of a high aspect ratio through-Si via (TSV). Formation of a thin barrier metal layer of Ni–B, Co–B and Co–W–B is possible using a Au nanoparticle (AuNP) catalyst, which is densely adsorbed on the SiO₂ of the TSV sidewall. A silane coupling agent of 3-aminopropyl-triethoxysilane is effective for enhancement of the density of adsorption for AuNP. A conformal electroless Cu layer is deposited on the barrier layer by displacement plating without a catalyst. The adhesion strength between the electroless barrier layer and the SiO₂ substrate is increased by annealing at 300 °C. These results strongly suggest that an all-wet process for the formation of Cu-filled TSV with a high aspect ratio is practically possible.     

Enclathration of hydrogen by organic-compound clathrate hydrates

The properties of hydrogen enclathration by cyclic ethers and acetone clathrate hydrates were investigated by powder X-ray diffraction, Raman spectroscopic analysis and volumetric analysis. Powder X-ray diffraction profiles indicate that the hydrates are structure-II hydrates. The variation in lattice constant by hydrogen occupation was investigated. This result indicates that inclusion of H₂ atom within empty small cage changes size of host cages depending on type of guest molecule. Raman results show that the samples formed binary clathrate hydrate of hydrogen and each organic compound. The amount of encaged H₂ was found to be comparable to that of H₂–THF binary hydrate. The trend of the changes for lattice constants is not related to the amount of encaged H₂. These results suggest that the organic compounds investigated in this study can be used as alternatives to THF for H₂ enclathration.     

A human-machine cooperative system for generating sign language animation using thermal image

We propose a new approach aimed at sign language animation by skin region detection on an infrared image. To generate several kinds of animations expressing personality and/or emotion appropriately, conventional systems require many manual operations. However, a promising way to realize a lower workload is to manually refine an animation made automatically with a dynamic image of real motion. In the proposed method, a 3D CG model corresponding to a characteristic posture in sign language is made automatically by pattern recognition on a thermal image, and then a person’s hand in the CG model is set. The hand part is made manually beforehand. If necessary, the model can be replaced manually by a more appropriate model corresponding to training key frames and/or the model can be refined manually. In our experiments, a person experienced in using sign language recognized the Japanese sign language of 71 words expressed as animation with 88.3% accuracy, and three persons experienced in using sign language also recognized the sign language animation representing three emotions (neutral, happy and angry) with 88.9% accuracy. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Design method for a new control system for an autonomous underwater vehicle using linear matrix inequalities

The independent administrative corporation Japan Agency for Marine–Earth Science and Technology (JAMSTEC) has developed a small light autonomous underwater vehicle (AUV) named marine robot experimental 1 (MR-X1).¹ The motion control of MR-X1 is considered in this article. Since the dynamics of MR-X1 mainly depends on its own speed, the motion control is a nonlinear control system. We propose a new controller design method for this system using linear matrix inequalities (LMIs). This algorithm gives a solution as a linear matrix inequality, and can be adapted to solve many LMIs simultaneously. LMIs can be obtained by substituting several speeds into the dynamics of the MR-X1. The proposed controller, which can be derived from the solution of the LMIs, was adapted to MR-X1 and showed good performance in experiments. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Development of a new underwater positioning system based on sensor network

In those days, the performance of underwater robot is improving because requirements for it are becoming higher and more complex than before. For those work, a highly precise and wide regional underwater positioning system is indispensable. However, the conventional positioning system that has three standard stations, is applicable for the field with no obstacle, because its principal depends on the time based measurement. Therefore, we developed a new underwater positioning system based on sensor network technology that is using a lot of standard stations equipping hydrophone and GPS sensor on the water surface. And decreasing the cost of sensor, we applied a simple method for measurement of distance, which is using sound power level. Therefore, this study has started with confirming the new principal for underwater positioning method. This paper is showing the availability with theoretical analysis and the performance of new method under the noise and reflection of sound in the water, and suggestions for the measures and an error compensation method. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Voltage control of distribution network with a large penetration of photovoltaic generation using FACTS devices

In recent years, there has been a great deal of interest in distributed generation from viewpoints of environmental problems and energy saving measures. Thus, a lot of distributed generators will be connected to the distribution network in the future. However, increase of distributed generators, which convert natural energy into electric energy, raises concerns as to their adverse effects on the distribution network. Therefore, control of distribution networks using Flexible AC Transmission System (FACTS) devices is considered in order to adjust the voltage profile, and as a result more distributed generations can be installed into the networks. In this paper, four types of FACTS devices— Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC), Unified Power Flow Controller (UPFC) and self‐commutated Back‐To‐Back converter (BTB)— are analyzed by comparison of required minimum capacity of the inverters in a residential distribution network with a large penetration of photovoltaic generations. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 165(3): 16– 28, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20499     

Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic neuron, and (3) weak synapses adapt more slowly than strong ones, while maintenance of strong synapses is costly. Our results show that a synaptic update rule derived from these principles shares features, with spike-timing-dependent plasticity, is sensitive to correlations in the input and is useful for synaptic memory. Moreover, input selectivity (sharply tuned receptive fields) of postsynaptic neurons develops only if stimuli with strong features are presented. Sharply tuned neurons can coexist with unselective ones, and the distribution of synaptic weights can be unimodal or bimodal. The formulation of synaptic dynamics through an optimality criterion provides a simple graphical argument for the stability of synapses, necessary for synaptic memory.     

Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.     

Location-free haptic interaction for large-area social applications

In this paper, we discuss the potential of force perception technologies for realizing hand-held devices in the field of social systems. We propose and develop an interactive force-sensation-based navigation system for waiters based on a force perception technology that we have proposed. The navigation system consists of our new hand-held haptic interface and a camera-based position and posture identification system. Since the proposed compact haptic interface does not require external grounding, it can be used outside the laboratory and does not interrupt human activity. We verify the feasibility of the system in trials where we collected the responses of system users.     

GMDH utilizing BDS with insufficient modelling data

It has been pointed out that the group method of data handling algorithm has a disadvantage that predicts an abnormal result in the event of shortage of modelling data. This paper presents a method of modelling that avoids the emergence of abnormal predicted values. For estimation of partial polynomial parameters, a bad data suppression estimator is employed. It is based on a non-quadratic cost function that reduces to the weighted least squares estimator in the absence of bad data. Real data are used to illustrate that this method prevents abnormal results.